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dc.contributor.authorWu, Zihuien_US
dc.contributor.authorSun, Yuen_US
dc.contributor.authorMatlock, Alexen_US
dc.contributor.authorLiu, Jiamingen_US
dc.contributor.authorTian, Leien_US
dc.contributor.authorKamilov, Ulugbeken_US
dc.date.accessioned2020-04-29T15:01:55Z
dc.date.available2020-04-29T15:01:55Z
dc.date.issued2019-11-29
dc.identifier.citationZihui Wu, Yu Sun, Alex Matlock, Jiaming Liu, Lei Tian, Ulugbek Kamilov. 2019. "SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors." arXiv:1911.13241,
dc.identifier.urihttps://hdl.handle.net/2144/40452
dc.description.abstractTwo features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixedpoint convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.en_US
dc.description.urihttps://arxiv.org/abs/1911.13241
dc.language.isoen_US
dc.relation.ispartofarXiv:1911.13241
dc.subjectOptical tomographyen_US
dc.subjectRegularization by denoisingen_US
dc.subjectPlug-and-play priorsen_US
dc.subjectStochastic optimizationen_US
dc.titleSIMBA: scalable inversion in optical tomography using deep denoising priorsen_US
dc.typeArticleen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.publication-statusPublished onlineen_US
dc.date.online2019-11-29
dc.identifier.orcid0000-0002-1316-4456 (Tian, Lei)
dc.description.oaversionFirst author draft
dc.identifier.mycv509101


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